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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Neural artistic style transfer


The first application we will implement is the neural artistic style transfer. Here, we will transfer the style of Van Gogh art onto an image. An image can be considered as a combination of style and content. The artistic style transfer technique transforms an image to look like a painting with a specific painting style. We will see how to code this idea up. The loss function will compare the generated image with the content of the photo and style of the painting. Hence, the optimization is carried out for the image pixel, rather than for the weights of the network. Two values are calculated by comparing the content of the photo with the generated image followed by the style of the painting and the generated image.

Content loss

Since pixels are not a good choice, we will use the CNN features of various layers, as they are a better representation of the content. The initial layers as seen in Chapter 3, Image Retrieval, have high-frequency such as edges, corners...

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